Editing Wireless sensor network (section)

==Other concepts==

===Localization===
Network localization refers to the problem of estimating the location of wireless sensor nodes during deployments and in dynamic settings. For ultra-low power sensors, size, cost and environment precludes the use of Global Positioning System receivers on sensors. In 2000, Nirupama Bulusu, [[John Heidemann]] and [[Deborah Estrin]] first motivated and proposed a radio connectivity based system for localization of wireless sensor networks.<ref>{{cite journal |last1=Bulusu |first1=Nirupama |last2=Heidemann |first2=John |last3=Estrin |first3=Deborah |title=GPS-less low cost outdoor localization for very small devices |journal=IEEE Personal Communications |year=2000 |volume=7 |issue=5 |pages=28–34 |url=https://ieeexplore.ieee.org/document/878533 |publisher=IEEE Personal Communications, October 2000|doi=10.1109/98.878533 |s2cid=771769 }}</ref> Subsequently, such localization systems have been referred to as range free localization systems, and many localization systems for wireless sensor networks have been subsequently proposed including AHLoS, APS, and Stardust.

===Sensor data calibration and fault tolerance===

Sensors and devices used in wireless sensor networks are state-of-the-art technology with the lowest possible price. The sensor measurements we get from these devices are therefore often noisy, incomplete and inaccurate. Researchers studying wireless sensor networks hypothesize that much more information can be extracted from hundreds of unreliable measurements spread across a field of interest than from a smaller number of high-quality, high-reliability instruments with the same total cost.

===Macroprogramming===
Macro-programming is a term coined by Matt Welsh.<ref>{{cite web|url=https://web.cecs.pdx.edu/~nbulusu/courses/cs497-win11/lectures/Lecture10-Welshnotes-macroprogramming.pdf|title=Programming Sensor Networks Using Abstract Regions|author1=Matt Welsh |author2=Geoff Mainland|publisher=Harvard University}}</ref> It refers to programming the entire sensor network as an ensemble, rather than individual sensor nodes. Another way to macro-program a network is to view the sensor network as a database, which was popularized by the TinyDB system developed by [[Sam Madden]].

=== Reprogramming ===
Reprogramming is the process of updating the code on the sensor nodes. The most feasible form of reprogramming is remote reprogramming whereby the code is disseminated wirelessly while the nodes are deployed. Different reprogramming protocols exist that provide different levels of speed of operation, reliability, energy expenditure, requirement of code resident on the nodes, suitability to different wireless environments, resistance to DoS, etc. Popular reprogramming protocols are Deluge (2004), Trickle (2004), MNP (2005), Synapse (2008), and Zephyr (2009).

=== Security ===
Infrastructure-less architecture (i.e. no gateways are included, etc.) and inherent requirements (i.e. unattended working environment, etc.) of WSNs might pose several weak points that attract adversaries. Therefore, [[Computer security|security]] is a big concern when WSNs are deployed for special applications such as military and healthcare. Owing to their unique characteristics, traditional security methods of [[computer network]]s would be useless (or less effective) for WSNs. Hence, lack of security mechanisms would cause intrusions towards those networks. These intrusions need to be detected and mitigation methods should be applied.

There have been important innovations in securing wireless sensor networks. Most wireless embedded networks use omni-directional  antennas and therefore neighbors can overhear communication in and out of nodes. This was used this to develop a primitive called "''local monitoring''"<ref>{{Cite book|last1=Khalil|first1=Issa|last2=Bagchi Saurabh|last3=Shroff|first3=N.B.|title=2005 International Conference on Dependable Systems and Networks (DSN'05) |chapter=LITEWORP: A Lightweight Countermeasure for the Wormhole Attack in Multihop Wireless Networks |date=2005|chapter-url=https://ieeexplore.ieee.org/document/1467835/;jsessionid=uTueYYCfo53VLLpCtnVIEiKbsdRLNoemV84PvINsBCUpJ-gq63t-!-1069638242|pages=612–621|doi=10.1109/DSN.2005.58|isbn=0-7695-2282-3|s2cid=2018708}}</ref> which was used for detection  of sophisticated attacks, like blackhole or wormhole, which degrade the  throughput of large networks to close-to-zero. This primitive has since been  used by many researchers and commercial wireless packet sniffers. This was subsequently refined for more sophisticated attacks such as with  collusion, mobility,  and multi-antenna, multi-channel devices.<ref>{{Cite journal|last1=Mitchell|first1=Robert|last2=Chen|first2=Ing-Ray|date=2014-04-01|title=A survey of intrusion detection in wireless network applications|url=http://www.sciencedirect.com/science/article/pii/S0140366414000280|journal=Computer Communications|language=en|volume=42|pages=1–23|doi=10.1016/j.comcom.2014.01.012|issn=0140-3664}}</ref>

===Distributed sensor network===
If a centralized architecture is used in a sensor network and the central node fails, then the entire network will collapse, however the reliability of the sensor network can be increased by using a distributed control architecture. Distributed control is used in WSNs for the following reasons:

# Sensor nodes are prone to failure,
# For better collection of data,
# To provide nodes with backup in case of failure of the central node.

There is also no centralised body to allocate the resources and they have to be self organized.

As for the distributed filtering over distributed sensor network. the general setup is to observe the underlying process through a group of sensors organized according to a given network topology, which renders the individual observer estimates the system state based not only on its own measurement but also on its neighbors'.<ref>{{Cite journal|last1=Li|first1=Wangyan|last2=Wang|first2=Zidong|last3=Wei|first3=Guoliang|last4=Ma|first4=Lifeng|last5=Hu|first5=Jun|last6=Ding|first6=Derui|date=2015|title=A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks|journal=Discrete Dynamics in Nature and Society|language=en|volume=2015|pages=1–12|doi=10.1155/2015/683701|issn=1026-0226|doi-access=free}}</ref>

===Data integration and sensor web===
{{main|Sensor web}}
The data gathered from wireless sensor networks is usually saved in the form of numerical data in a central base station. Additionally, the [[Open Geospatial Consortium]] (OGC) is specifying standards for interoperability interfaces and metadata encodings that enable real time integration of heterogeneous sensor webs into the Internet, allowing any individual to monitor or control wireless sensor networks through a web browser.

===In-network processing===
To reduce communication costs some algorithms remove or reduce nodes' redundant sensor information and avoid forwarding data that is of no use. This technique has been used, for instance, for distributed anomaly detection<ref>{{Cite journal|last1=Bosman|first1=H. H. W. J.|last2=Iacca|first2=G|last3=Tejada|first3=A.|last4=Wörtche|first4=H. J.|last5=Liotta|first5=A.|date=2015-12-01|title=Ensembles of incremental learners to detect anomalies in ad hoc sensor networks|journal=Ad Hoc Networks|series=Special Issue on Big Data Inspired Data Sensing, Processing and Networking Technologies|volume=35|pages=14–36|doi=10.1016/j.adhoc.2015.07.013|issn=1570-8705|hdl=11572/196409|hdl-access=free}}</ref><ref>{{Cite book|last1=Bosman|first1=H. H. W. J.|last2=Liotta|first2=A.|last3=Iacca|first3=G.|last4=Wörtche|first4=H. J.|title=2013 IEEE International Conference on Systems, Man, and Cybernetics |chapter=Anomaly Detection in Sensor Systems Using Lightweight Machine Learning |date=October 2013|pages=7–13|doi=10.1109/SMC.2013.9|isbn=978-1-4799-0652-9|s2cid=6434158}}</ref><ref>{{Cite book|last1=Bosman|first1=H. H. W. J.|last2=Liotta|first2=A.|last3=Iacca|first3=G.|last4=Wörtche|first4=H. J.|title=2013 IEEE 13th International Conference on Data Mining Workshops |chapter=Online Extreme Learning on Fixed-Point Sensor Networks |date=December 2013|pages=319–326|doi=10.1109/ICDMW.2013.74|isbn=978-1-4799-3142-2|s2cid=6460187}}</ref><ref>{{Cite book|last1=Bosman|first1=H. H. W. J.|last2=Iacca|first2=G.|last3=Wörtche|first3=H. J.|last4=Liotta|first4=A.|title=2014 IEEE International Conference on Data Mining Workshop |chapter=Online Fusion of Incremental Learning for Wireless Sensor Networks |date=December 2014|pages=525–532|doi=10.1109/ICDMW.2014.79|isbn=978-1-4799-4274-9|hdl=10545/622629|s2cid=14029568|hdl-access=free}}</ref> or distributed optimization.<ref>{{Cite journal|last=Iacca|first=G.|title=Distributed optimization in wireless sensor networks: an island-model framework|journal=Soft Computing|language=en|volume=17|issue=12|pages=2257–2277|doi=10.1007/s00500-013-1091-x|issn=1433-7479|arxiv=1810.02679|year=2018|bibcode=2018arXiv181002679I|s2cid=33273544}}</ref> As nodes can inspect the data they forward, they can measure averages or directionality for example of readings from other nodes. For example, in sensing and monitoring applications, it is generally the case that neighboring sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires techniques for in-network data aggregation and mining. Aggregation reduces the amount of network traffic which helps to reduce energy consumption on sensor nodes.<ref>{{Cite journal|last1=Bosman|first1=H. H. W. J.|last2=Iacca|first2=G.|last3=Tejada|first3=A.|last4=Wörtche|first4=H. J.|last5=Liotta|first5=A.|date=2017-01-01|title=Spatial anomaly detection in sensor networks using neighborhood information|journal=Information Fusion|volume=33|pages=41–56|doi=10.1016/j.inffus.2016.04.007|issn=1566-2535|doi-access=free|hdl=11572/196405|hdl-access=free}}</ref><ref name="refESPDA">{{Cite book|last=Cam|first=H|author2=Ozdemir, S Nair, P Muthuavinashiappan, D|title=Proceedings of IEEE Sensors 2003 (IEEE Cat. No.03CH37498)|chapter=ESPDA: Energy-efficient and Secure Pattern-based Data Aggregation for wireless sensor networks|date=October 2003|volume=2|pages=732–736|doi=10.1109/icsens.2003.1279038|isbn=978-0-7803-8133-9|citeseerx=10.1.1.1.6961|s2cid=15686293}}</ref> Recently, it has been found that network gateways also play an important role in improving energy efficiency of sensor nodes by scheduling more resources for the nodes with more critical energy efficiency need and advanced energy efficient scheduling algorithms need to be implemented at network gateways for the improvement of the overall network energy efficiency.<ref name=Zander/><ref>{{cite journal|last=Rowayda|first=A. Sadek|title= Hybrid energy aware clustered protocol for IoT heterogeneous network |journal= Future Computing and Informatics Journal|volume=3|issue=2|pages=166–177|date=May 2018 |doi=10.1016/j.fcij.2018.02.003|doi-access=free}}</ref>

===Secure data aggregation===
This is a form of in-network processing where [[sensor node]]s are assumed to be unsecured with limited available energy, while the base station is assumed to be secure with unlimited available energy. Aggregation complicates the already existing security challenges for wireless sensor networks<ref name="refSAWN">{{cite journal|last=Hu|first=Lingxuan|author2=David Evans |title=Secure aggregation for wireless networks|journal=Workshop on Security and Assurance in Ad Hoc Networks|date=January 2003}}</ref> and requires new security techniques tailored specifically for these scenarios. Providing security to aggregate data in wireless sensor networks is known as ''secure data aggregation in WSN''.<ref name="refESPDA" /><ref name="refSAWN" /><ref name="refSIA">{{cite book|last=Przydatek|first=Bartosz|author2=Dawn Song |author3=Adrian Perrig |title=Proceedings of the 1st international conference on Embedded networked sensor systems |chapter=SIA: Secure information aggregation in sensor networks|year=2003|pages=255–265|doi=10.1145/958491.958521|isbn=978-1-58113-707-1|s2cid=239370}}</ref> were the first few works discussing techniques for secure  data aggregation in wireless sensor networks.

Two main security challenges in secure data aggregation are confidentiality and integrity of data. While [[encryption]] is traditionally used to provide end to end confidentiality in wireless sensor network, the aggregators in a secure data aggregation scenario need to decrypt the encrypted data to perform aggregation. This exposes the plaintext at the aggregators, making the data vulnerable to attacks from an adversary. Similarly an aggregator can inject false data into the aggregate and make the base station accept false data. Thus, while data aggregation improves energy efficiency of a network, it complicates the existing security challenges.<ref name=refSDA>{{cite book|last=Kumar|first=Vimal|author2=Sanjay K. Madria |title=2012 IEEE 13th International Conference on Mobile Data Management|chapter=Secure Hierarchical Data Aggregation in Wireless Sensor Networks: Performance Evaluation and Analysis|date=August 2012 |pages=196–201|doi=10.1109/MDM.2012.49|isbn=978-1-4673-1796-2|s2cid=2990744}}</ref>